Discover gists
| 8 тиждень - SPRINT 7 (05.10 - 11.10): Документація, оптимізація та деплой | |
| Складання технічної документації проєкту | |
| Оформлення README.md-файла в репозиторії (список пунктів: https://gist.github.com/sunmeat/5b4d38b2277b5c13c0e4857f07c34c9d) | |
| Створення коментарів документації до ключових компонентів системи (основні публічні класи та публічні методи) | |
| Сирцевий код обробити в програмах накшталт DocFX / Doxygen, отримати сайт з навігацією, покласти його в папку docs репозиторію | |
| Цей сайт з документацією викласти на Github Pages / Netlify / Vercel | |
| Створити документацію по енд-поінтах через Scalar / Swashbuckle, з публікацією | |
| Оптимізація коду для підвищення продуктивності | |
| Проведення повного тестування системи | |
| Виправлення виявлених помилок і багів |
| # DSA Question Bank | |
| ## Arrays, Hashing, Prefix/Suffix | |
| 1. Find the second largest element in one pass without extra space. | |
| 2. Find the k largest distinct elements without fully sorting the array. | |
| 3. Find the two numbers that appear once when every other number appears twice. | |
| 4. Find the majority element that appears more than n / 2 times. | |
| 5. Find all elements that appear more than n / 3 times. | |
| 6. Count inversions in an array. |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Needs Docker Desktop (Kubernetes enabled), Helm and k6 (brew install k6).
scripts/deploy-local.sh # build + deploy app, Postgres, migrations → localhost:8080
kubectl -n checkout apply -f k8s/monitoring/prometheus.yaml # Prometheus → localhost:9090
kubectl -n checkout exec deploy/checkout-api-postgres -- psql -U postgres -c "UPDATE items SET stock = 1000000;"опис
You are setting up a local LLM stack on an AMD Strix Halo machine (Ryzen AI Max+ 395, Radeon 8060S, gfx1151, 128 GB unified memory) running native Linux. When you finish, the user can start any of these from LlamaStash (TUI, CLI, or its OpenAI/Anthropic proxy), each with ready-made presets:
| LlamaStash row | Engine | Weights |
|---|---|---|
flash-next-halogen |
Halogen (Docker image) | Halogen native v2 .hgn (Qwen3.8-Flash-Next) |
Qwen3.8-Flash-Next-UD-Q4_K_XL |
gufo (native build) | Unsloth UD-Q4_K_XL GGUF + Unsloth shared MTP head |
Qwen3.8-27B-UD-Q6_K |
gufo (native build) | Unsloth UD-Q6_K GGUF + z-lab DFlash2 draft |
DNS Proxy is a simple DNS proxy server that supports all existing DNS protocols including DNS-over-TLS, DNS-over-HTTPS, DNSCrypt, and DNS-over-QUIC. Moreover, it can work as a DNS-over-HTTPS, DNS-over-TLS or DNS-over-QUIC server.
VERSION=$(curl -s https://api.github.com/repos/AdguardTeam/dnsproxy/releases/latest | grep tag_name | cut -d '"' -f 4) && echo "Latest AdguardTeam dnsproxy version is $VERSION"
wget -O dnsproxy.tar.gz "https://github.com/AdguardTeam/dnsproxy/releases/download/${VERSION}/dnsproxy-linux-amd64-${VERSION}.tar.gz"
tar -xzvf dnsproxy.tar.gz
cd linux-amd64
See how a minor change to your commit message style can make a difference.
git commit -m"<type>(<optional scope>): <description>" \ -m"<optional body>" \
| Filter | Description | Example |
|---|---|---|
| allintext | Searches for occurrences of all the keywords given. | allintext:"keyword" |
| intext | Searches for the occurrences of keywords all at once or one at a time. | intext:"keyword" |
| inurl | Searches for a URL matching one of the keywords. | inurl:"keyword" |
| allinurl | Searches for a URL matching all the keywords in the query. | allinurl:"keyword" |
| intitle | Searches for occurrences of keywords in title all or one. | intitle:"keyword" |
